MCP HubMCP Hub
スキル一覧に戻る

deal-momentum-analyzer

OneWave-AI
更新日 Today
39 閲覧
11
4
11
GitHubで表示
コミュニケーションai

について

このスキルは、メールの返信時間やミーティングの頻度などのセールスエンゲージメントデータを分析し、商談の勢いスコアを算出します。成約が見込まれる商談と停滞する可能性のある商談を予測し、実行可能な提言を提供します。開発者はこれを利用して、販売予測やパイプライン分析機能を構築できます。

クイックインストール

Claude Code

推奨
プラグインコマンド推奨
/plugin add https://github.com/OneWave-AI/claude-skills
Git クローン代替
git clone https://github.com/OneWave-AI/claude-skills.git ~/.claude/skills/deal-momentum-analyzer

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

Deal Momentum Analyzer

Score deal velocity based on email response times, meeting frequency, and stakeholder engagement. Predict which deals will close vs stall.

Instructions

You are an expert at sales analytics and deal forecasting. Analyze deal engagement patterns, calculate momentum scores, and predict close probability with action recommendations.

Output Format

# Deal Momentum Analyzer Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

Best Practices

  1. Be Specific: Focus on concrete, actionable outputs
  2. Use Templates: Provide copy-paste ready formats
  3. Include Examples: Show real-world usage
  4. Add Context: Explain why recommendations matter
  5. Stay Current: Use latest best practices for sales

Common Use Cases

Trigger Phrases:

  • "Help me with [use case]"
  • "Generate [output type]"
  • "Create [deliverable]"

Example Request:

"[Sample user request here]"

Response Approach:

  1. Understand user's context and goals
  2. Generate comprehensive output
  3. Provide actionable recommendations
  4. Include examples and templates
  5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

GitHub リポジトリ

OneWave-AI/claude-skills
パス: deal-momentum-analyzer

関連スキル

evaluating-llms-harness

テスト

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

スキルを見る

sglang

メタ

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

スキルを見る

cloudflare-turnstile

メタ

This skill provides comprehensive guidance for implementing Cloudflare Turnstile as a CAPTCHA-alternative bot protection system. It covers integration for forms, login pages, API endpoints, and frameworks like React/Next.js/Hono, while handling invisible challenges that maintain user experience. Use it when migrating from reCAPTCHA, debugging error codes, or implementing token validation and E2E tests.

スキルを見る

langchain

メタ

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

スキルを見る